1. Technical Field
The present invention relates to landmark detection in medical images.
2. Discussion of the Related Art
In the area of medical image analysis, anatomical landmark detection often plays a fundamental and critical role. High level image analysis and understanding, e.g., segmentation [M. Rousson, A. Khamene, M. Diallo, J. C. Celi, and F. Sauer, “Constrained surface evolutions for prostate and bladder segmentation in ct images”, in Computer Vision for Biomedical Image Applications, pp. 251-260, 2005], registration [Y. Zhan, Y. Ou, M. Feldman, J. Tomaszeweski, C. Davatzikos, and D. Shen, “Registering histologic and mr images of prostate for image-based cancer detection”, Academic Radiology, 14, pp. 1367-1381, 2007] and computational anatomy [X. Tao, C. Davatsikos, and J. L. Prince, “Using the fast marching method to extract curves with given global properties”, in Medical Image Computing and Computer-Assisted Intervention, pp. 870-877, 2007], usually starts from the identification and localization of anatomical landmarks in medical images. The accuracy of landmark detection thus becomes critical to the performance of medical image understanding.
During the last decade, methods of anatomical landmark detection have been extensively investigated. Ehrhardt et al. [J. Ehrhardt, H. Handels, B. Strathmann, T. Malina, W. Ploetz, and S. Poeppl, “Atlas-based recognition of anatomical structures and landmarks to support the virtual three-dimensional planning of hip operations”, in Medical Image Computing and Computer-Assisted Intervention, pp. 17-24, 2003] used a surface based registration algorithm for detecting anatomical landmarks as an aiding mechanism for planning hip operations. A template based matching method of landmark points in the chest was used as the initial registration step in an application of nodule registration [M. Betke, H. Hong, D. Thomas, C. Prince, and J. P. Ko, “Landmark detection in the chest and registration of lung surfaces with an application to nodule registration”, Medical Image Analysis 7, pp. 265-281, 2003]. Bodke et al. [A. Bodke, S. Teipel, Y. Zebuhr, G. Lesinger, L. Gootjes, R. Schwarz, K. Buerger, P. Scheltens, H. Moeller, and H. Hampel, “A new rapid landmark-based regional mri segmentation method of the brain”, Journal of the Neurological Science 194, pp. 35-40, 2002] proposed a landmark-based method for segmenting four cerebral lobes of the brain in magnetic resonance imaging (M RI) scans. A clustering based method was introduced in [G. Zimmerman, S. Gordon, and H. Greenspan, “Automatic landmark detection in uterine cervix images for indexing in a content-retrieval system”, in EE International Symposium on Biomedical Imaging, April 2006] for detecting cervix boundary and os, in uterine cervix images. Although these methods achieve success in specific studies, they lack in generalization capability since their design incorporates the inherent structural information that needs to be detected.
In the area of general computer vision, a widely researched topic in object detection literature is detection of faces. Several algorithms have been proposed, all of which are aimed to couple feature extraction with strong statistical pattern classification methods such as support vector machines [E. Osuna, R. Freund, and F. Girosi, “Training support vector machines: An application to face detection”, IEEE Conference On Computer Vision and Pattern Recognition, pp. 130-136, 1997], fisher linear Discriminant analysis [M. Yang, N. Ahuja, and D. Kreigman, “Face detection using mixtures of linear subspaces”, in Face and Gesture Recognition, p. 7076, 2000], neural networks [H. Rowley, S. Baluja, and T. Kanade, “Neural network-based face detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence 7 no. 1, p. 2338, 1998], multi layer perceptrons [K. Sung and T. Poggio, “Example-based learning for view-based human face detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence 20 no. 1, pp. 39-51, 1998] and finally a cascade of boosted weak classifiers [P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features”, in IEEE Conference on Computer Vision and Pattern Recognition 2001, p. 511, 2001].